MPRAsnakeflow experiment QC report

Good Spearman correlation across replicates!

The data has a good Spearman correlations across replicates using a barcode threshold per oligo of 10! Spearman correlation of DNA is 1.00, RNA is 0.98 and DNA/RNA ratio is 0.96.

Overall quality metrics

Table explanation

median rna read count: Median of RNA read count for oligos that passed filtering to determine sufficient coverage in terms of read count. Value is the median of all replicates.
median barcodes passing filtering: Median number of barcodes across tested sequences that passed filtering to determine if there was sufficient barcode to oligo coverage. Value is the median of all replicates.
pearson correlation: The correlation of log2 RNA/DNA ratios across tested sequences as a measure of replicable activity signal. Value is the median of replicate comparisons using only oligos with >= 10 barcodes.
fraction oligos passing: Fraction of tested sequences that passed filtering of the mappable sequences to determine if the designed library was sufficiently recovered. Value is the median of all replicates and using only oligos with >= 10 barcodes.

median barcodes passing filtering	median rna read count	pearson correlation	fraction oligos passing
176	12224	0.97	0.96

DNA over RNA counts

Plotting normalized counts of DNA vs RNA (median across replicates). Only oligos within all replicates are shown. We should see a variation within the RNA count data (along the y axis). If count data between RNA and DNA is highly correlated (e.g. follows the identity line) there is no variation between designed oligos. This is an indication that RNA is inflated with DNA and the DNA digestion before creating cDNA did not work as expected.

Oligo correlation

Oligo correlation plots of DNA, RNA and DNA/RNA ratios across replicates. First tab shows plots using (in average) 2302 oligos with a minimum number of 10 barcodes. Second tab shows all 2391 oligos that have assigned barcodes.

Condition	A	B	#Oligos A	#Oligos B	#Oligos Joined	DNA spearman	RNA spearman	Ratio spearman	DNA log2 pearson	RNA log2 pearson	Ratio log2 pearson
HEPG2	1	2	2303	2302	2302	1.00	0.98	0.96	1.00	0.98	0.96
HEPG2	1	3	2303	2303	2303	1.00	0.98	0.97	1.00	0.98	0.97
HEPG2	2	3	2302	2303	2302	1.00	1.00	0.99	1.00	1.00	0.99

Condition	A	B	#Oligos A	#Oligos B	#Oligos Joined	DNA spearman	RNA spearman	Ratio spearman	DNA log2 pearson	RNA log2 pearson	Ratio log2 pearson
HEPG2	1	2	2391	2391	2391	0.99	0.98	0.96	0.98	0.98	0.95
HEPG2	1	3	2391	2391	2391	1.00	0.98	0.97	0.99	0.98	0.97
HEPG2	2	3	2391	2391	2391	0.99	0.99	0.99	0.99	0.99	0.98

Experiment statistic

The total number of oligos in this experiment is 2398 (defined by the assignment) with 938503 associated barcodes.

In average across replicates we see 2391 from 458820 average barcodes in the count data and around 377578 barcodes where not in the assignment.

condition	replicate	oligos dna/rna	matched barcodes	unknown barcodes dna/rna	% matched barcodes	total dna counts	total rna counts	avg dna counts per bc	avg rna counts per bc	avg dna/rna barcodes per oligo
HEPG2	1	2391	458306	376572	54.89	21004963	55170372	25.16	66.08	191.68
HEPG2	2	2391	455280	295548	60.64	20013815	36062281	26.66	48.03	190.41
HEPG2	3	2391	462874	460614	50.12	29614804	55689525	32.07	60.30	193.59

Experiment	Barcodes	Counts	Average counts	Assigned barcodes	Assigned counts	Average assigned counts	Fraction assigned barcodes	Fraction assigned counts
HEPG2.1.DNA	1712727	21963798	12.82	469743	17029998	36.25	0.27	0.78
HEPG2.2.DNA	1643842	20988683	12.77	467728	16298744	34.85	0.28	0.78
HEPG2.3.DNA	2111923	30931142	14.65	477864	24014862	50.25	0.23	0.78
HEPG2.1.RNA	2779709	57511512	20.69	487105	43586626	89.48	0.18	0.76
HEPG2.2.RNA	2198921	37761918	17.17	482087	28543781	59.21	0.22	0.76
HEPG2.3.RNA	2777366	57875305	20.84	487857	43863504	89.91	0.18	0.76

Histograms barcodes per oligo, counts per barcode

Histogramm of number of barcodes per oligo and the number of counts per barcode devidied by DNA and RNA. Median is red, mean is blue.

Activity

Violin and box plots of the log2 fold change of all oligos by the assay. Grouped by labels if set, otherwise NA. First tab shows plots using (in average) 2302 oligos with a minimum number of 10 barcodes. Second tab shows all 2391 oligos that have assigned barcodes.

--- title: "MPRAsnakeflow experiment QC report" page-layout: full format: html: code-fold: true code-tools: true embed-resources: true anchor-sections: true smooth-scroll: true toc: true theme: light: flatly dark: darkly jupyter: python3 --- ```{python} #| echo: false # Imports from IPython.display import display, Image, Markdown import pandas as pd import json ``` ```{python} #| tags: [parameters] #| echo: false ``` ```{python} #| echo: false oligo_cor_merged_table_thresh = pd.read_csv(f"{workdir}/{statistics_all_oligo_cor_thresh}", sep='\t') oligo_cor_merged_table_all = pd.read_csv(f"{workdir}/{statistics_all_oligo_cor_all}", sep='\t') dna_spear = oligo_cor_merged_table_thresh['DNA_spearman'].min() rna_spear = oligo_cor_merged_table_thresh['RNA_spearman'].min() ratio_spear = oligo_cor_merged_table_thresh['Ratio_spearman'].min() ``` ```{python} #| echo: false threshold_dna_spear = 0.85 threshold_rna_spear = 0.95 threshold_ratio_spear = 0.75 important = dna_spear < threshold_dna_spear and rna_spear < threshold_rna_spear and ratio_spear < threshold_ratio_spear warning = dna_spear < threshold_dna_spear or rna_spear < threshold_rna_spear or ratio_spear < threshold_ratio_spear if important: display(Markdown(f''' ::: {"{.callout-important}"} ## Low correlation! Spearman correlation across replicates is low using a barcode threshold per oligo of {thresh}! Spearman correlation of DNA is {dna_spear:.2f}, RNA is {rna_spear:.2f} and DNA/RNA ratio is {ratio_spear:.2f}. Minimum allowed values are {threshold_dna_spear:.2f}, {threshold_rna_spear:.2f}, and {threshold_ratio_spear:.2f}. ::: ''')) elif warning: display(Markdown(f''' ::: {"{.callout-warning}"} ## Low correlation warning! At least one (not all) Spearman correlations across replicates is low using a barcode threshold per oligo of {thresh}! Spearman correlation of DNA is {dna_spear:.2f}, RNA is {rna_spear:.2f} and DNA/RNA ratio is {ratio_spear:.2f}. Minimum allowed values are {threshold_dna_spear:.2f}, {threshold_rna_spear:.2f}, and {threshold_ratio_spear:.2f}. ::: ''')) else: display(Markdown(f''' ::: {"{.callout-tip}"} ## Good Spearman correlation across replicates! The data has a good Spearman correlations across replicates using a barcode threshold per oligo of {thresh}! Spearman correlation of DNA is {dna_spear:.2f}, RNA is {rna_spear:.2f} and DNA/RNA ratio is {ratio_spear:.2f}. ::: ''')) ``` # Overall quality metrics <details> <summary>Table explanation</summary> - **median rna read count:** Median of RNA read count for oligos that passed filtering to determine sufficient coverage in terms of read count. Value is the median of all replicates. - **median barcodes passing filtering:** Median number of barcodes across tested sequences that passed filtering to determine if there was sufficient barcode to oligo coverage. Value is the median of all replicates. - **pearson correlation:** The correlation of log2 RNA/DNA ratios across tested sequences as a measure of replicable activity signal. Value is the median of replicate comparisons using only oligos with >= `{python} thresh` barcodes. - **fraction oligos passing:** Fraction of tested sequences that passed filtering of the mappable sequences to determine if the designed library was sufficiently recovered. Value is the median of all replicates and using only oligos with >= `{python} thresh` barcodes. </details> ```{python} #| echo: false with open(f"{workdir}/{qc_metrics_file}", 'r') as file: qc_data = json.load(file) df_qc = pd.DataFrame([qc_data]) df_qc.columns = df_qc.columns.str.replace("_", " ", regex=False) #df_qc.columns = ['Value'] df_qc.style.hide(axis="index").format(precision=2) ``` # DNA over RNA counts Plotting normalized counts of DNA vs RNA (median across replicates). Only oligos within all replicates are shown. We should see a variation within the RNA count data (along the y axis). If count data between RNA and DNA is highly correlated (e.g. follows the identity line) there is no variation between designed oligos. This is an indication that RNA is inflated with DNA and the DNA digestion before creating cDNA did not work as expected. ::: {.panel-tabset} ## Threshold `{python} thresh` ```{python} #| echo: false Image(f"{workdir}/{dna_over_rna_thresh_plot}") ``` ## All ```{python} #| echo: false Image(f"{workdir}/{dna_over_rna_plot}") ``` ::: # Oligo correlation Oligo correlation plots of DNA, RNA and DNA/RNA ratios across replicates. First tab shows plots using (in average) `{python} "%d" % oligo_cor_merged_table_thresh['number_Oligos_Joined'].mean()` oligos with a minimum number of `{python} thresh` barcodes. Second tab shows all `{python} "%d" % oligo_cor_merged_table_all['number_Oligos_Joined'].mean()` oligos that have assigned barcodes. ```{python} #| echo: false def filter_table(df): df = df.round(2).drop(columns=['DNA_pearson','RNA_pearson', 'Ratio_pearson', 'fraction_Oligos_ReplicateA', 'fraction_Oligos_ReplicateB', 'NormSymmetry']) df.columns = df.columns.str.replace("_", " ", regex=False).str.replace("Replicate", " ", regex=False).str.replace("number ", "#", regex=False) return(df) ``` ::: {.panel-tabset} ## Threshold `{python} thresh` ```{python} #| echo: false oligo_cor_merged_table_thresh_filter = filter_table(oligo_cor_merged_table_thresh) oligo_cor_merged_table_thresh_filter.style.hide(axis="index").format( precision=2) ``` ## All ```{python} #| echo: false oligo_cor_merged_table_all_filter = filter_table(oligo_cor_merged_table_all) oligo_cor_merged_table_all_filter.style.hide(axis="index").format( precision=2) ``` ::: ::: {.panel-tabset} ## Threshold `{python} thresh` ::: {.panel-tabset} ### DNA ```{python} #| echo: false display(Image(f"{workdir}/{dna_oligo_coor_min_thre_plot }")) ``` ### RNA ```{python} #| echo: false display(Image(f"{workdir}/{rna_oligo_coor_min_thre_plot}")) ``` ### Ratio ```{python} #| echo: false display(Image(f"{workdir}/{ratio_oligo_min_thre_plot}")) ``` ::: ## All ::: {.panel-tabset} ### DNA ```{python} #| echo: false display(Image(f"{workdir}/{dna_oligo_coor_plot}")) ``` ### RNA ```{python} #| echo: false display(Image(f"{workdir}/{rna_oligo_coor_plot}")) ``` ### Ratio ```{python} #| echo: false display(Image(f"{workdir}/{ratio_oligo_coor_plot}")) ``` ::: ::: # Experiment statistic ```{python} #| echo: false df_merged = pd.read_csv(f"{workdir}/{statistics_all_merged}", sep='\t').round(2) n_oligos = df_merged['oligos design'].iloc[0] n_barcodes = df_merged['barcodes design'].iloc[0] avg_oligos = df_merged['oligos dna/rna'].mean() avg_matched_barcodes = df_merged['matched barcodes'].mean() avg_unknown_barcodes = df_merged['unknown barcodes dna/rna'].mean() ``` The total number of oligos in this experiment is `{python} f"{n_oligos}"` (defined by the assignment) with `{python} f"{n_barcodes}"` associated barcodes. In average across replicates we see `{python} f"{avg_oligos:.0f}"` from `{python} f"{avg_matched_barcodes:.0f}"` average barcodes in the count data and around `{python} f"{avg_unknown_barcodes:.0f}"` barcodes where not in the assignment. ::: {.panel-tabset} # DNA/RNA merged ```{python} #| echo: false df_merged.drop(columns=['oligos design','barcodes design', 'barcodes dna/rna'], inplace=True) df_merged.style.hide(axis="index").format( precision=2) ``` # DNA/RNA individual ```{python} #| echo: false df_single = pd.read_csv(f"{workdir}/{statistics_all_single}", sep='\t').round(2) df_single.style.hide(axis="index").format( precision=2) ``` ::: # Histograms barcodes per oligo, counts per barcode Histogramm of number of barcodes per oligo and the number of counts per barcode devidied by DNA and RNA. Median is red, mean is blue. ::: {.panel-tabset} # Number of barcodes per oligo ```{python} #| echo: false display(Image(f"{workdir}/{counts_per_oligo}")) ``` # Number of counts per DNA barcode ```{python} #| echo: false display(Image(f"{workdir}/{counts_per_bc_dna}")) ``` # Number of counts per RNA barcode ```{python} #| echo: false display(Image(f"{workdir}/{counts_per_bc_rna}")) ``` ::: # Activity Violin and box plots of the log2 fold change of all oligos by the assay. Grouped by labels if set, otherwise `NA`. First tab shows plots using (in average) `{python} "%d" % oligo_cor_merged_table_thresh['number_Oligos_Joined'].mean()` oligos with a minimum number of `{python} thresh` barcodes. Second tab shows all `{python} "%d" % oligo_cor_merged_table_all['number_Oligos_Joined'].mean()` oligos that have assigned barcodes. ::: {.panel-tabset} ## Threshold `{python} thresh` ```{python} #| echo: false display(Image(f"{workdir}/{activity_thresh}")) ``` ## All ```{python} #| echo: false display(Image(f"{workdir}/{activity_all}")) ``` :::